


import argparse
import re

import requests
import torch

# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from transformers import (
    BertTokenizer,
    BlipConfig,
    BlipForConditionalGeneration,
    BlipForImageTextRetrieval,
    BlipForQuestionAnswering,
)







def 			SCREAMING_SNAKE_CASE_		(     __A					:     List[str]    ,       __A					:     Dict							)      ->						Optional[int]:

       """simple docstring"""

       a_   :						Optional[Any]				=    'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
       a_   :						str				=    Image.open(requests.get(__lowerCAmelCase    ,       stream=__lowerCAmelCase							).raw							).convert('RGB'							)

       a_   :						List[Any]				=    transforms.Compose(
           [
               transforms.Resize((image_size, image_size)    ,       interpolation=InterpolationMode.BICUBIC							),
               transforms.ToTensor(),
               transforms.Normalize((0.48145466, 0.4578275, 0.40821073)    ,       (0.26862954, 0.26130258, 0.27577711)							),
           ]							)
       a_   :						Optional[int]				=    transform(__lowerCAmelCase							).unsqueeze(0							).to(__lowerCAmelCase							)
       return image







def 			SCREAMING_SNAKE_CASE_		(     __A					:     Tuple							)      ->						Optional[Any]:

       """simple docstring"""

       if "visual_encoder" in key:
              a_   :						List[str]				=    re.sub('visual_encoder*'    ,       'vision_model.encoder'    ,       __lowerCAmelCase							)
       if "blocks" in key:
              a_   :						Any				=    re.sub(R'blocks'    ,       'layers'    ,       __lowerCAmelCase							)
       if "attn" in key:
              a_   :						Tuple				=    re.sub(R'attn'    ,       'self_attn'    ,       __lowerCAmelCase							)
       if "norm1" in key:
              a_   :						Dict				=    re.sub(R'norm1'    ,       'layer_norm1'    ,       __lowerCAmelCase							)
       if "norm2" in key:
              a_   :						Union[str, Any]				=    re.sub(R'norm2'    ,       'layer_norm2'    ,       __lowerCAmelCase							)
       if "encoder.norm" in key:
              a_   :						Any				=    re.sub(R'encoder.norm'    ,       'post_layernorm'    ,       __lowerCAmelCase							)
       if "encoder.patch_embed.proj" in key:
              a_   :						Union[str, Any]				=    re.sub(R'encoder.patch_embed.proj'    ,       'embeddings.patch_embedding'    ,       __lowerCAmelCase							)

       if "encoder.pos_embed" in key:
              a_   :						Optional[int]				=    re.sub(R'encoder.pos_embed'    ,       'embeddings.position_embedding'    ,       __lowerCAmelCase							)
       if "encoder.cls_token" in key:
              a_   :						Optional[int]				=    re.sub(R'encoder.cls_token'    ,       'embeddings.class_embedding'    ,       __lowerCAmelCase							)

       if "self_attn" in key:
              a_   :						Optional[int]				=    re.sub(R'self_attn.proj'    ,       'self_attn.projection'    ,       __lowerCAmelCase							)

       return key







@torch.no_grad()
def 			SCREAMING_SNAKE_CASE_		(     __A					:     Any    ,       __A					:     List[Any]=None							)      ->						Optional[int]:

       """simple docstring"""

       if config_path is not None:
              a_   :						Union[str, Any]				=    BlipConfig.from_pretrained(__lowerCAmelCase							)
       else:
              a_   :						Dict				=    BlipConfig(projection_dim=5_12    ,       text_config={}    ,       vision_config={}							)

       a_   :						Optional[int]				=    BlipForConditionalGeneration(__lowerCAmelCase							).eval()

       a_   :						List[Any]				=    'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'

       a_   :						List[str]				=    blip_decoder(pretrained=__lowerCAmelCase    ,       image_size=3_84    ,       vit='base'							)
       a_   :						List[str]				=    pt_model.eval()

       a_   :						List[str]				=    pt_model.state_dict()
       for key in modified_state_dict.copy():
              a_   :						Dict				=    modified_state_dict.pop(__lowerCAmelCase							)
              a_   :						Union[str, Any]				=    rename_key(__lowerCAmelCase							)
              a_   :						Optional[int]				=    value

       hf_model.load_state_dict(__lowerCAmelCase							)

       a_   :						Tuple				=    3_84
       a_   :						int				=    load_demo_image(image_size=__lowerCAmelCase    ,       device='cpu'							)
       a_   :						str				=    BertTokenizer.from_pretrained('bert-base-uncased'							)
       a_   :						int				=    tokenizer(['a picture of']							).input_ids

       a_   :						Dict				=    hf_model.generate(__lowerCAmelCase    ,       __lowerCAmelCase							)

       assert out[0].tolist() == [3_05_22, 10_37, 38_61, 19_97, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

       a_   :						Optional[int]				=    hf_model.generate(__lowerCAmelCase							)

       assert out[0].tolist() == [3_05_22, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

       if pytorch_dump_folder_path is not None:
              hf_model.save_pretrained(__lowerCAmelCase							)

       # model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
       a_   :						Union[str, Any]				=    (
           'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'
       )

       a_   :						Optional[int]				=    blip_vqa(pretrained=__lowerCAmelCase    ,       image_size=__lowerCAmelCase    ,       vit='base'							)
       vqa_model.eval()

       a_   :						Optional[int]				=    vqa_model.state_dict()
       for key in modified_state_dict.copy():
              a_   :						int				=    modified_state_dict.pop(__lowerCAmelCase							)
              a_   :						List[Any]				=    rename_key(__lowerCAmelCase							)
              a_   :						Union[str, Any]				=    value

       a_   :						Tuple				=    BlipForQuestionAnswering(__lowerCAmelCase							)

       hf_vqa_model.load_state_dict(__lowerCAmelCase							)

       a_   :						int				=    ['How many dogs are in this image?']
       a_   :						int				=    tokenizer(__lowerCAmelCase    ,       return_tensors='pt'							).input_ids

       a_   :						Union[str, Any]				=    hf_vqa_model.generate(__lowerCAmelCase    ,       __lowerCAmelCase							)
       print(tokenizer.decode(answer[0]							)							)

       assert tokenizer.decode(answer[0]							) == "[UNK] 1 [SEP]"
       if pytorch_dump_folder_path is not None:
              hf_vqa_model.save_pretrained(pytorch_dump_folder_path + '_vqa'							)

       a_   :						str				=    'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'

       a_   :						str				=    blip_itm(pretrained=__lowerCAmelCase    ,       image_size=__lowerCAmelCase    ,       vit='base'							)
       itm_model.eval()

       a_   :						Dict				=    itm_model.state_dict()
       for key in modified_state_dict.copy():
              a_   :						List[Any]				=    modified_state_dict.pop(__lowerCAmelCase							)
              a_   :						Tuple				=    rename_key(__lowerCAmelCase							)
              a_   :						List[Any]				=    value

       a_   :						Union[str, Any]				=    BlipForImageTextRetrieval(__lowerCAmelCase							)

       a_   :						int				=    ['A picture of a woman with a dog sitting in a beach']
       a_   :						Optional[Any]				=    tokenizer(
           __lowerCAmelCase    ,       return_tensors='pt'    ,       padding='max_length'    ,       truncation=__lowerCAmelCase    ,       max_length=35    ,       ).input_ids

       hf_itm_model.load_state_dict(__lowerCAmelCase							)
       hf_itm_model.eval()

       a_   :						Optional[Any]				=    hf_itm_model(__lowerCAmelCase    ,       __lowerCAmelCase    ,       use_itm_head=__lowerCAmelCase							)
       a_   :						int				=    hf_itm_model(__lowerCAmelCase    ,       __lowerCAmelCase    ,       use_itm_head=__lowerCAmelCase							)

       assert out[0].item() == 0.2110687494277954
       assert torch.nn.functional.softmax(out_itm[0]    ,       dim=1							)[:, 1].item() == 0.45698845386505127

       if pytorch_dump_folder_path is not None:
              hf_itm_model.save_pretrained(pytorch_dump_folder_path + '_itm'							)


if __name__ == "__main__":
   UpperCAmelCase_ :					Union[str, Any]       =			argparse.ArgumentParser()
   parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
   parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
   UpperCAmelCase_ :					Any       =			parser.parse_args()

   convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)



"""simple docstring"""


def      lowerCamelCase_					(   __lowerCAmelCase							)				->   int:




						'''simple docstring'''



						if not isinstance(__lowerCAmelCase  ,       __lowerCAmelCase							):
												raise TypeError("only integers accepted as input"							)
						else:
												lowerCamelCase__   =str(abs(__lowerCAmelCase							)							)
												lowerCamelCase__   =[list(__lowerCAmelCase							) for char in range(len(__lowerCAmelCase							)							)]
												for index in range(len(__lowerCAmelCase							)							):
																		num_transpositions[index].pop(__lowerCAmelCase							)
												return max(
												    int("".join(list(__lowerCAmelCase							)							)							) for transposition in num_transpositions							)


if __name__ == "__main__":
	__import__('doctest').testmod()


